Wstęp
Największą przeszkodą w analizowanym zbiorze danych był jego rozmiar.Od samego wczytywania danych, poprzez proces czyszczenia, aż do budowania klasyfikatora należało pamiętać o skończonej ilości czasu i pamięci jaką się dysponuje. Pierwsze próby trenowania klasyfikatorów takich jak kNN czy Random Forest zakończyły się niepowodzeniem z powodu zbyt długiego czasu wykonania. Aby przyspieszyć proces najpierw wybrałem prostszy algorytm regresji liniowej i usunąłem ze zbioru danych atrybuty o niewielkim odstępie międzykwartylowym, zakładając że niewiele wniosą do klasyfikacji czy regresji. Następnie zacząłem usuwać po jednym atrybucie z każdej pary atrybutów mocno skorelowanych. W ten sposób ograniczyłem zbiór danych do 8 atrybutów i 3 etykiet. Pozwoliło to wykorzystać algorytm Random Forest do klasyfikacji, a także bardzo szybko liczyć regresję liniową. Niestety jakość modeli, który powstał w ten sposób pozostawia wiele do życzenia. Próba przybliżenia liczby elektronów czy atomów skończyła się utworzeniem modelu o dość niskim współczynniku R^2 (ok. 0.43), co może wskazywać na nieliniowość tego zjawiska lub błędny proces oczyszczania danych. Jeszcze gorzej zakończyła się próba przypisania danej obserwacji do odpowiadającej cząsteczki. Dokładność wyniosła jedynie ok. 27%. Podjąłem dodatkowo próbę zbudowania klasyfikatora, który na wejściu będzie przyjmował wyjście ze zbudowanych wcześniej przeze mnie modeli określających liczbę atomów i elektronów. Niestety ta próba zakończyła się poprawnym sklasyfikowaniem jedynie 16% przykładów.
Wczytanie i wstępne przetwarzanie danych
Wykorzystywanie biblioteki
library(dplyr)
library(ggplot2)
library(tidyr)
library(caret)
library(scales)
library(plotly)
library(knitr)
library(kableExtra)
Zapewnienie powtarzalności analizy
set.seed(23)
Wczytwanie danych z pliku
knownClasses <- c("title"="character", "blob_coverage"="character", "res_coverage"="character", "skeleton_data"="character")
initial <- read.csv(file="C:/Users/wilcz/OneDrive/Pulpit/all_summary/all_summary.csv", header=TRUE, sep=";", nrows = 75000, colClasses=knownClasses)
classes <- sapply(initial, class)
rm(initial)
All_Data <- read.csv(file="C:/Users/wilcz/OneDrive/Pulpit/all_summary/all_summary.csv", header=TRUE, sep=";",colClasses = classes)
Usuwanie nieporządanych res_name
res_names_to_drop <- c("UNK", "UNX", "UNL", "DUM", "N", "BLOB", "ALA", "ARG", "ASN", "ASP", "CYS", "GLN", "GLU", "GLY", "HIS", "ILE", "LEU", "LYS", "MET", "MSE", "PHE", "PRO", "SEC", "SER", "THR", "TRP", "TYR", "VAL", "DA", "DG", "DT", "DC", "DU", "A", "G", "T", "C", "U", "HOH", "H20", "WAT")
All_Data <- All_Data %>% filter(!(res_name %in% res_names_to_drop) )
Uzupełnianie brakujących wartości
has_conflicted_res_name <- function(observation) {
!is.na(observation$res_name) & observation$name_from_title != as.character(observation$res_name)
}
tmp_name <- All_Data[, c("title","res_name")]
tmp_name$name_from_title <- sapply(tmp_name$title, function(x) { strsplit(x," ")[[1]][2] })
kable(tmp_name[has_conflicted_res_name(tmp_name),]) %>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px") #sprawdzenie czy gdzieś występują niespójności w nazwach
|
title
|
res_name
|
name_from_title
|
#nie występują, więc możemy nadpisać kolumnę res_name wartościami z tytulu
kable(head(tmp_name[is.na(tmp_name$res_name),]))%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px") # ZONK sód (NA) jest traktowany jako wartość pusta! takich wartości jest 10K
|
|
title
|
res_name
|
name_from_title
|
|
639
|
4fxz NA 602 A
|
NA
|
NA
|
|
640
|
4fxz NA 603 A
|
NA
|
NA
|
|
901
|
5fkf NA 1108 A
|
NA
|
NA
|
|
902
|
5fkf NA 1105 A
|
NA
|
NA
|
|
1038
|
5j2b NA 404 A
|
NA
|
NA
|
|
1039
|
5j2b NA 405 A
|
NA
|
NA
|
levels(tmp_name$res_name) <- c( levels(tmp_name$res_name), "NA")
tmp_name[is.na(tmp_name$res_name) & tmp_name$name_from_title == "NA","res_name"] <- "NA"
kable(summary(All_Data$res_name))%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px") #9.6k NAs
|
|
x
|
|
SO4
|
56572
|
|
GOL
|
40606
|
|
EDO
|
30825
|
|
NAG
|
26360
|
|
CL
|
23223
|
|
CA
|
21038
|
|
ZN
|
19826
|
|
MG
|
14779
|
|
HEM
|
11192
|
|
PO4
|
11090
|
|
ACT
|
8096
|
|
DMS
|
6633
|
|
IOD
|
6317
|
|
PEG
|
4987
|
|
CLA
|
4784
|
|
K
|
4706
|
|
FAD
|
4555
|
|
NAD
|
4501
|
|
MN
|
4215
|
|
ADP
|
3819
|
|
MLY
|
3509
|
|
NAP
|
3505
|
|
CD
|
3242
|
|
MPD
|
3221
|
|
FMT
|
2918
|
|
MAN
|
2841
|
|
PG4
|
2768
|
|
MES
|
2697
|
|
CU
|
2353
|
|
ATP
|
2296
|
|
COA
|
2183
|
|
1PE
|
2136
|
|
BR
|
2127
|
|
NDP
|
2106
|
|
FMN
|
2084
|
|
EPE
|
1933
|
|
HEC
|
1917
|
|
PGE
|
1905
|
|
TRS
|
1656
|
|
SF4
|
1647
|
|
NI
|
1637
|
|
ACY
|
1609
|
|
FE
|
1602
|
|
NO3
|
1596
|
|
PLP
|
1594
|
|
GDP
|
1589
|
|
SAH
|
1587
|
|
FE2
|
1560
|
|
SEP
|
1491
|
|
CIT
|
1464
|
|
BME
|
1419
|
|
ANP
|
1404
|
|
BOG
|
1387
|
|
C8E
|
1369
|
|
BMA
|
1335
|
|
GSH
|
1282
|
|
LDA
|
1278
|
|
GLC
|
1219
|
|
OLC
|
1186
|
|
ACE
|
1154
|
|
GTP
|
1133
|
|
BGC
|
1120
|
|
AMP
|
1114
|
|
TPO
|
1106
|
|
IPA
|
1092
|
|
P6G
|
1084
|
|
CO
|
1041
|
|
IMD
|
1039
|
|
CSO
|
1038
|
|
FES
|
1034
|
|
GAL
|
1017
|
|
PTR
|
1000
|
|
LLP
|
992
|
|
CME
|
961
|
|
HG
|
950
|
|
SIA
|
906
|
|
MRD
|
902
|
|
KCX
|
897
|
|
SAM
|
864
|
|
HYP
|
852
|
|
BCL
|
848
|
|
LMT
|
835
|
|
FLC
|
809
|
|
CDL
|
795
|
|
CYC
|
783
|
|
ACO
|
748
|
|
NCO
|
747
|
|
UDP
|
725
|
|
BCR
|
721
|
|
PCA
|
717
|
|
SCN
|
685
|
|
MLI
|
674
|
|
CSD
|
651
|
|
FUC
|
627
|
|
NAI
|
624
|
|
TLA
|
623
|
|
NDG
|
618
|
|
CAS
|
617
|
|
(Other)
|
156807
|
|
NA’s
|
9613
|
All_Data$res_name <- tmp_name$res_name
kable(summary(All_Data$res_name))%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px") # 0 NAs - victory!
|
|
x
|
|
SO4
|
56572
|
|
GOL
|
40606
|
|
EDO
|
30825
|
|
NAG
|
26360
|
|
CL
|
23223
|
|
CA
|
21038
|
|
ZN
|
19826
|
|
MG
|
14779
|
|
HEM
|
11192
|
|
PO4
|
11090
|
|
NA
|
9613
|
|
ACT
|
8096
|
|
DMS
|
6633
|
|
IOD
|
6317
|
|
PEG
|
4987
|
|
CLA
|
4784
|
|
K
|
4706
|
|
FAD
|
4555
|
|
NAD
|
4501
|
|
MN
|
4215
|
|
ADP
|
3819
|
|
MLY
|
3509
|
|
NAP
|
3505
|
|
CD
|
3242
|
|
MPD
|
3221
|
|
FMT
|
2918
|
|
MAN
|
2841
|
|
PG4
|
2768
|
|
MES
|
2697
|
|
CU
|
2353
|
|
ATP
|
2296
|
|
COA
|
2183
|
|
1PE
|
2136
|
|
BR
|
2127
|
|
NDP
|
2106
|
|
FMN
|
2084
|
|
EPE
|
1933
|
|
HEC
|
1917
|
|
PGE
|
1905
|
|
TRS
|
1656
|
|
SF4
|
1647
|
|
NI
|
1637
|
|
ACY
|
1609
|
|
FE
|
1602
|
|
NO3
|
1596
|
|
PLP
|
1594
|
|
GDP
|
1589
|
|
SAH
|
1587
|
|
FE2
|
1560
|
|
SEP
|
1491
|
|
CIT
|
1464
|
|
BME
|
1419
|
|
ANP
|
1404
|
|
BOG
|
1387
|
|
C8E
|
1369
|
|
BMA
|
1335
|
|
GSH
|
1282
|
|
LDA
|
1278
|
|
GLC
|
1219
|
|
OLC
|
1186
|
|
ACE
|
1154
|
|
GTP
|
1133
|
|
BGC
|
1120
|
|
AMP
|
1114
|
|
TPO
|
1106
|
|
IPA
|
1092
|
|
P6G
|
1084
|
|
CO
|
1041
|
|
IMD
|
1039
|
|
CSO
|
1038
|
|
FES
|
1034
|
|
GAL
|
1017
|
|
PTR
|
1000
|
|
LLP
|
992
|
|
CME
|
961
|
|
HG
|
950
|
|
SIA
|
906
|
|
MRD
|
902
|
|
KCX
|
897
|
|
SAM
|
864
|
|
HYP
|
852
|
|
BCL
|
848
|
|
LMT
|
835
|
|
FLC
|
809
|
|
CDL
|
795
|
|
CYC
|
783
|
|
ACO
|
748
|
|
NCO
|
747
|
|
UDP
|
725
|
|
BCR
|
721
|
|
PCA
|
717
|
|
SCN
|
685
|
|
MLI
|
674
|
|
CSD
|
651
|
|
FUC
|
627
|
|
NAI
|
624
|
|
TLA
|
623
|
|
NDG
|
618
|
|
CAS
|
617
|
|
(Other)
|
156807
|
rm(tmp_name)
Rozmiar zbioru i podstawowe statystyki.
kable(dim(All_Data))%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
kable(summary(All_Data))%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
|
|
blob_coverage
|
res_coverage
|
title </th>
|
pdb_code </th>
|
res_name </th>
|
res_id </th>
|
chain_id </th>
|
blob_volume_coverage
|
blob_volume_coverage_second
|
res_volume_coverage
|
res_volume_coverage_second
|
local_res_atom_count
|
local_res_atom_non_h_count
|
local_res_atom_non_h_occupancy_sum
|
local_res_atom_non_h_electron_sum
|
local_res_atom_non_h_electron_occupancy_sum
|
local_res_atom_C_count
|
local_res_atom_N_count
|
local_res_atom_O_count
|
local_res_atom_S_count
|
dict_atom_non_h_count
|
dict_atom_non_h_electron_sum
|
dict_atom_C_count
|
dict_atom_N_count
|
dict_atom_O_count
|
dict_atom_S_count
|
skeleton_data
|
skeleton_cycle_4
|
skeleton_diameter
|
skeleton_cycle_6
|
skeleton_cycle_7
|
skeleton_closeness_006_008
|
skeleton_closeness_002_004
|
skeleton_cycle_3
|
skeleton_avg_degree
|
skeleton_closeness_004_006
|
skeleton_closeness_010_012
|
skeleton_closeness_012_014
|
skeleton_edges
|
skeleton_radius
|
skeleton_cycle_8_plus
|
skeleton_closeness_020_030
|
skeleton_deg_5_plus
|
skeleton_closeness_016_018
|
skeleton_closeness_008_010
|
skeleton_closeness_018_020
|
skeleton_average_clustering
|
skeleton_closeness_040_050
|
skeleton_closeness_014_016
|
skeleton_center
|
skeleton_closeness_000_002
|
skeleton_density
|
skeleton_closeness_030_040
|
skeleton_deg_4
|
skeleton_deg_0
|
skeleton_deg_1
|
skeleton_deg_2
|
skeleton_deg_3
|
skeleton_graph_clique_number
|
skeleton_nodes
|
skeleton_cycles
|
skeleton_cycle_5
|
skeleton_closeness_050_plus
|
skeleton_periphery
|
local_volume
|
local_electrons
|
local_mean
|
local_std
|
local_min
|
local_max
|
local_max_over_std
|
local_skewness
|
local_cut_by_mainchain_volume
|
local_near_cut_count_C
|
local_near_cut_count_other
|
local_near_cut_count_S
|
local_near_cut_count_O
|
local_near_cut_count_N
|
part_00_shape_segments_count
|
part_00_density_segments_count
|
part_00_volume
|
part_00_electrons
|
part_00_mean
|
part_00_std
|
part_00_max
|
part_00_max_over_std
|
part_00_skewness
|
part_00_parts
|
part_00_shape_O3
|
part_00_shape_O4
|
part_00_shape_O5
|
part_00_shape_FL
|
part_00_shape_O3_norm
|
part_00_shape_O4_norm
|
part_00_shape_O5_norm
|
part_00_shape_FL_norm
|
part_00_shape_I1
|
part_00_shape_I2
|
part_00_shape_I3
|
part_00_shape_I4
|
part_00_shape_I5
|
part_00_shape_I6
|
part_00_shape_I1_norm
|
part_00_shape_I2_norm
|
part_00_shape_I3_norm
|
part_00_shape_I4_norm
|
part_00_shape_I5_norm
|
part_00_shape_I6_norm
|
part_00_shape_M000
|
part_00_shape_CI
|
part_00_shape_E3_E1
|
part_00_shape_E2_E1
|
part_00_shape_E3_E2
|
part_00_shape_sqrt_E1
|
part_00_shape_sqrt_E2
|
part_00_shape_sqrt_E3
|
part_00_density_O3
|
part_00_density_O4
|
part_00_density_O5
|
part_00_density_FL
|
part_00_density_O3_norm
|
part_00_density_O4_norm
|
part_00_density_O5_norm
|
part_00_density_FL_norm
|
part_00_density_I1
|
part_00_density_I2
|
part_00_density_I3
|
part_00_density_I4
|
part_00_density_I5
|
part_00_density_I6
|
part_00_density_I1_norm
|
part_00_density_I2_norm
|
part_00_density_I3_norm
|
part_00_density_I4_norm
|
part_00_density_I5_norm
|
part_00_density_I6_norm
|
part_00_density_M000
|
part_00_density_CI
|
part_00_density_E3_E1
|
part_00_density_E2_E1
|
part_00_density_E3_E2
|
part_00_density_sqrt_E1
|
part_00_density_sqrt_E2
|
part_00_density_sqrt_E3
|
part_00_shape_Z_7_3
|
part_00_shape_Z_0_0
|
part_00_shape_Z_7_0
|
part_00_shape_Z_7_1
|
part_00_shape_Z_3_0
|
part_00_shape_Z_5_2
|
part_00_shape_Z_6_1
|
part_00_shape_Z_3_1
|
part_00_shape_Z_6_0
|
part_00_shape_Z_2_1
|
part_00_shape_Z_6_3
|
part_00_shape_Z_2_0
|
part_00_shape_Z_6_2
|
part_00_shape_Z_5_0
|
part_00_shape_Z_5_1
|
part_00_shape_Z_4_2
|
part_00_shape_Z_1_0
|
part_00_shape_Z_4_1
|
part_00_shape_Z_7_2
|
part_00_shape_Z_4_0
|
part_00_density_Z_7_3
|
part_00_density_Z_0_0
|
part_00_density_Z_7_0
|
part_00_density_Z_7_1
|
part_00_density_Z_3_0
|
part_00_density_Z_5_2
|
part_00_density_Z_6_1
|
part_00_density_Z_3_1
|
part_00_density_Z_6_0
|
part_00_density_Z_2_1
|
part_00_density_Z_6_3
|
part_00_density_Z_2_0
|
part_00_density_Z_6_2
|
part_00_density_Z_5_0
|
part_00_density_Z_5_1
|
part_00_density_Z_4_2
|
part_00_density_Z_1_0
|
part_00_density_Z_4_1
|
part_00_density_Z_7_2
|
part_00_density_Z_4_0
|
part_01_shape_segments_count
|
part_01_density_segments_count
|
part_01_volume
|
part_01_electrons
|
part_01_mean
|
part_01_std
|
part_01_max
|
part_01_max_over_std
|
part_01_skewness
|
part_01_parts
|
part_01_shape_O3
|
part_01_shape_O4
|
part_01_shape_O5
|
part_01_shape_FL
|
part_01_shape_O3_norm
|
part_01_shape_O4_norm
|
part_01_shape_O5_norm
|
part_01_shape_FL_norm
|
part_01_shape_I1
|
part_01_shape_I2
|
part_01_shape_I3
|
part_01_shape_I4
|
part_01_shape_I5
|
part_01_shape_I6
|
part_01_shape_I1_norm
|
part_01_shape_I2_norm
|
part_01_shape_I3_norm
|
part_01_shape_I4_norm
|
part_01_shape_I5_norm
|
part_01_shape_I6_norm
|
part_01_shape_M000
|
part_01_shape_CI
|
part_01_shape_E3_E1
|
part_01_shape_E2_E1
|
part_01_shape_E3_E2
|
part_01_shape_sqrt_E1
|
part_01_shape_sqrt_E2
|
part_01_shape_sqrt_E3
|
part_01_density_O3
|
part_01_density_O4
|
part_01_density_O5
|
part_01_density_FL
|
part_01_density_O3_norm
|
part_01_density_O4_norm
|
part_01_density_O5_norm
|
part_01_density_FL_norm
|
part_01_density_I1
|
part_01_density_I2
|
part_01_density_I3
|
part_01_density_I4
|
part_01_density_I5
|
part_01_density_I6
|
part_01_density_I1_norm
|
part_01_density_I2_norm
|
part_01_density_I3_norm
|
part_01_density_I4_norm
|
part_01_density_I5_norm
|
part_01_density_I6_norm
|
part_01_density_M000
|
part_01_density_CI
|
part_01_density_E3_E1
|
part_01_density_E2_E1
|
part_01_density_E3_E2
|
part_01_density_sqrt_E1
|
part_01_density_sqrt_E2
|
part_01_density_sqrt_E3
|
part_01_shape_Z_7_3
|
part_01_shape_Z_0_0
|
part_01_shape_Z_7_0
|
part_01_shape_Z_7_1
|
part_01_shape_Z_3_0
|
part_01_shape_Z_5_2
|
part_01_shape_Z_6_1
|
part_01_shape_Z_3_1
|
part_01_shape_Z_6_0
|
part_01_shape_Z_2_1
|
part_01_shape_Z_6_3
|
part_01_shape_Z_2_0
|
part_01_shape_Z_6_2
|
part_01_shape_Z_5_0
|
part_01_shape_Z_5_1
|
part_01_shape_Z_4_2
|
part_01_shape_Z_1_0
|
part_01_shape_Z_4_1
|
part_01_shape_Z_7_2
|
part_01_shape_Z_4_0
|
part_01_density_Z_7_3
|
part_01_density_Z_0_0
|
part_01_density_Z_7_0
|
part_01_density_Z_7_1
|
part_01_density_Z_3_0
|
part_01_density_Z_5_2
|
part_01_density_Z_6_1
|
part_01_density_Z_3_1
|
part_01_density_Z_6_0
|
part_01_density_Z_2_1
|
part_01_density_Z_6_3
|
part_01_density_Z_2_0
|
part_01_density_Z_6_2
|
part_01_density_Z_5_0
|
part_01_density_Z_5_1
|
part_01_density_Z_4_2
|
part_01_density_Z_1_0
|
part_01_density_Z_4_1
|
part_01_density_Z_7_2
|
part_01_density_Z_4_0
|
part_02_shape_segments_count
|
part_02_density_segments_count
|
part_02_volume
|
part_02_electrons
|
part_02_mean
|
part_02_std
|
part_02_max
|
part_02_max_over_std
|
part_02_skewness
|
part_02_parts
|
part_02_shape_O3
|
part_02_shape_O4
|
part_02_shape_O5
|
part_02_shape_FL
|
part_02_shape_O3_norm
|
part_02_shape_O4_norm
|
part_02_shape_O5_norm
|
part_02_shape_FL_norm
|
part_02_shape_I1
|
part_02_shape_I2
|
part_02_shape_I3
|
part_02_shape_I4
|
part_02_shape_I5
|
part_02_shape_I6
|
part_02_shape_I1_norm
|
part_02_shape_I2_norm
|
part_02_shape_I3_norm
|
part_02_shape_I4_norm
|
part_02_shape_I5_norm
|
part_02_shape_I6_norm
|
part_02_shape_M000
|
part_02_shape_CI
|
part_02_shape_E3_E1
|
part_02_shape_E2_E1
|
part_02_shape_E3_E2
|
part_02_shape_sqrt_E1
|
part_02_shape_sqrt_E2
|
part_02_shape_sqrt_E3
|
part_02_density_O3
|
part_02_density_O4
|
part_02_density_O5
|
part_02_density_FL
|
part_02_density_O3_norm
|
part_02_density_O4_norm
|
part_02_density_O5_norm
|
part_02_density_FL_norm
|
part_02_density_I1
|
part_02_density_I2
|
part_02_density_I3
|
part_02_density_I4
|
part_02_density_I5
|
part_02_density_I6
|
part_02_density_I1_norm
|
part_02_density_I2_norm
|
part_02_density_I3_norm
|
part_02_density_I4_norm
|
part_02_density_I5_norm
|
part_02_density_I6_norm
|
part_02_density_M000
|
part_02_density_CI
|
part_02_density_E3_E1
|
part_02_density_E2_E1
|
part_02_density_E3_E2
|
part_02_density_sqrt_E1
|
part_02_density_sqrt_E2
|
part_02_density_sqrt_E3
|
part_02_shape_Z_7_3
|
part_02_shape_Z_0_0
|
part_02_shape_Z_7_0
|
part_02_shape_Z_7_1
|
part_02_shape_Z_3_0
|
part_02_shape_Z_5_2
|
part_02_shape_Z_6_1
|
part_02_shape_Z_3_1
|
part_02_shape_Z_6_0
|
part_02_shape_Z_2_1
|
part_02_shape_Z_6_3
|
part_02_shape_Z_2_0
|
part_02_shape_Z_6_2
|
part_02_shape_Z_5_0
|
part_02_shape_Z_5_1
|
part_02_shape_Z_4_2
|
part_02_shape_Z_1_0
|
part_02_shape_Z_4_1
|
part_02_shape_Z_7_2
|
part_02_shape_Z_4_0
|
part_02_density_Z_7_3
|
part_02_density_Z_0_0
|
part_02_density_Z_7_0
|
part_02_density_Z_7_1
|
part_02_density_Z_3_0
|
part_02_density_Z_5_2
|
part_02_density_Z_6_1
|
part_02_density_Z_3_1
|
part_02_density_Z_6_0
|
part_02_density_Z_2_1
|
part_02_density_Z_6_3
|
part_02_density_Z_2_0
|
part_02_density_Z_6_2
|
part_02_density_Z_5_0
|
part_02_density_Z_5_1
|
part_02_density_Z_4_2
|
part_02_density_Z_1_0
|
part_02_density_Z_4_1
|
part_02_density_Z_7_2
|
part_02_density_Z_4_0
|
fo_col </th>
|
fc_col </th>
|
weight_col
|
grid_space
|
solvent_radius
|
solvent_opening_radius
|
resolution_max_limit
|
resolution
|
FoFc_mean
|
FoFc_std </th>
|
FoFc_square_std
|
FoFc_min </th>
|
FoFc_max </th>
|
part_step_FoFc_std_min
|
part_step_FoFc_std_max
|
part_step_FoFc_std_step
|
|
|
Length:585339
|
Length:585339
|
Length:585339
|
4xk8 : 868
|
SO4 : 56572
|
301 : 19436
|
A :277258
|
Min. :0.02004
|
Min. :0.0000
|
Min. :0.001884
|
Min. :0.00000
|
Min. : 1.0
|
Min. : 1.00
|
Min. : -7.38
|
Min. : 3.0
|
Min. :-45.91
|
Min. : 0.000
|
Min. : 0.000
|
Min. : 0.000
|
Min. : 0.0000
|
Min. : 1.00
|
Min. : 3
|
Min. : 0.000
|
Min. : 0.000
|
Min. : 0.000
|
Min. : 0.000
|
Length:585339
|
Min. : 0.0000
|
Min. : 0.00
|
Min. : 0.00000
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 0.0000
|
Min. : 0.0000
|
Min. :0.000
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 0.000
|
Min. : 0.00
|
Min. : 0.00
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 0.000
|
Min. : 0.000
|
Min. :0.0000000
|
Min. : 0.000
|
Min. : 0.00
|
Min. : 1.000
|
Min. : 0.0000
|
Min. :0.00000
|
Min. : 0.000
|
Min. : 0.0000
|
Min. :0.0000
|
Min. : 0.000
|
Min. : 0.00
|
Min. : 0.000
|
Min. :1.000
|
Min. : 1.00
|
Min. : 0.00
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 1.000
|
Min. : 49.25
|
Min. : 0.0117
|
Min. :0.0001147
|
Min. :0.0006606
|
Min. :0
|
Min. : 0.00452
|
Min. : 2.836
|
Min. :0.001174
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 0.00000
|
Min. : 0.0000
|
Min. : 0.00
|
Min. : 0.000
|
Min. : 0.0
|
Min. : 0.0
|
Min. : 0.00
|
Min. : 0.000
|
Min. :0.0000
|
Min. :0.00000
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 0.00000
|
Min. : 0.000
|
Min. :1.210e+02
|
Min. :3.807e+03
|
Min. :3.378e+04
|
Min. :7.600e+01
|
Min. : 0.2306
|
Min. :0.01756
|
Min. :0.000426
|
Min. : 0.00000
|
Min. :4.800e+02
|
Min. :5.694e+04
|
Min. :4.999e+04
|
Min. :4.200e+01
|
Min. :2.000e+00
|
Min. :2.234e+04
|
Min. : 0.0632
|
Min. : 0.00104
|
Min. : 0
|
Min. : 0.00000
|
Min. : 0.00000
|
Min. : 0.00
|
Min. : 38
|
Min. :-129.45458
|
Min. :0.000066
|
Min. :0.000129
|
Min. :0.01144
|
Min. : 1.076
|
Min. : 0.7389
|
Min. : 0.5959
|
Min. : 10
|
Min. :2.400e+01
|
Min. :1.700e+01
|
Min. :-3.000e+00
|
Min. : 0.0356
|
Min. : 0.00042
|
Min. : 0.000002
|
Min. : -0.035
|
Min. :4.200e+01
|
Min. :3.630e+02
|
Min. :5.040e+02
|
Min. :-1.000e+00
|
Min. :0.000e+00
|
Min. :1.830e+02
|
Min. : 0.00
|
Min. : 0.000
|
Min. :0.000e+00
|
Min. : -0.012
|
Min. : 0.000
|
Min. : 0
|
Min. : 1.86
|
Min. :-155.70141
|
Min. :0.000066
|
Min. :0.000128
|
Min. :0.01305
|
Min. : 1.072
|
Min. : 0.7382
|
Min. : 0.5955
|
Min. : 6.303
|
Min. : 3.012
|
Min. : 0.6818
|
Min. : 3.662
|
Min. : 0.5714
|
Min. : 4.58
|
Min. : 1.808
|
Min. : 2.506
|
Min. : 0.02436
|
Min. : 2.43
|
Min. : 4.114
|
Min. : 1.05
|
Min. : 2.941
|
Min. : 0.7929
|
Min. : 3.464
|
Min. : 3.537
|
Min. :0.7373
|
Min. : 1.95
|
Min. : 5.742
|
Min. : 0.02276
|
Min. : 2.895
|
Min. : 0.6671
|
Min. : 0.9849
|
Min. : 2.877
|
Min. : 0.4221
|
Min. : 2.151
|
Min. : 0.4344
|
Min. : 1.445
|
Min. : 0.00707
|
Min. : 0.7737
|
Min. : 0.5449
|
Min. : 0.3607
|
Min. : 0.4772
|
Min. : 0.8741
|
Min. : 2.142
|
Min. : 0.5863
|
Min. :0.6768
|
Min. : 0.4739
|
Min. : 2.887
|
Min. : 0.00739
|
Min. : 0.0
|
Min. : 0.0
|
Min. : 0.000
|
Min. : 0.000
|
Min. :0.0000
|
Min. :0.00000
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 0.00000
|
Min. : 0.000
|
Min. :7.500e+01
|
Min. :1.819e+03
|
Min. :1.334e+04
|
Min. :0.000e+00
|
Min. : 0.227
|
Min. : 0.017
|
Min. :0.000
|
Min. : 0.000
|
Min. :2.110e+02
|
Min. :1.092e+04
|
Min. :9.455e+03
|
Min. :0.000e+00
|
Min. :0.000e+00
|
Min. :5.359e+03
|
Min. : 0.061
|
Min. : 0.001
|
Min. : 0
|
Min. : 0.000
|
Min. : 0.000
|
Min. : 0.00
|
Min. : 32
|
Min. :-142.643
|
Min. :0.000
|
Min. :0.000
|
Min. :0.009
|
Min. : 0.930
|
Min. : 0.528
|
Min. : 0.303
|
Min. : 3
|
Min. :2.000e+00
|
Min. :1.000e+00
|
Min. :-2.000e+01
|
Min. : 0.035
|
Min. : 0.000
|
Min. : 0.000
|
Min. : -0.041
|
Min. :1.800e+01
|
Min. :7.500e+01
|
Min. :7.500e+01
|
Min. :-5.000e+00
|
Min. :0.000e+00
|
Min. :1.600e+01
|
Min. : 0.00
|
Min. : 0.00
|
Min. :0.000e+00
|
Min. : -0.015
|
Min. : 0.000
|
Min. : 0
|
Min. : 0.47
|
Min. :-162.580
|
Min. :0.000
|
Min. :0.000
|
Min. :0.010
|
Min. : 0.925
|
Min. : 0.527
|
Min. : 0.303
|
Min. : 4.606
|
Min. : 2.764
|
Min. : 0.706
|
Min. : 3.418
|
Min. : 0.628
|
Min. : 3.097
|
Min. : 0.794
|
Min. : 2.425
|
Min. : 0.008
|
Min. : 1.713
|
Min. : 3.401
|
Min. : 0.052
|
Min. : 2.461
|
Min. : 0.745
|
Min. : 2.414
|
Min. : 2.22
|
Min. :0.704
|
Min. : 1.138
|
Min. : 4.023
|
Min. : 0.000
|
Min. : 2.597
|
Min. : 0.334
|
Min. : 0.620
|
Min. : 1.906
|
Min. : 0.440
|
Min. : 2.074
|
Min. : 0.196
|
Min. : 1.001
|
Min. : 0.007
|
Min. : 0.356
|
Min. : 0.361
|
Min. : 0.051
|
Min. : 0.266
|
Min. : 0.678
|
Min. : 1.927
|
Min. : 0.27
|
Min. :0.625
|
Min. : 0.195
|
Min. : 2.256
|
Min. : 0.005
|
Min. : 0.0
|
Min. : 0.0
|
Min. : 0.000
|
Min. : 0.000
|
Min. :0.0000
|
Min. :0.00000
|
Min. : 0.0000
|
Min. : 0.000
|
Min. : 0.00000
|
Min. : 0.0
|
Min. :7.400e+01
|
Min. :1.809e+03
|
Min. :1.229e+04
|
Min. :-6.100e+01
|
Min. : 0.23
|
Min. : 0.02
|
Min. :0.00
|
Min. : 0.00
|
Min. :2.060e+02
|
Min. :1.088e+04
|
Min. :9.187e+03
|
Min. :-2.200e+01
|
Min. :0.000e+00
|
Min. :5.232e+03
|
Min. : 0.06
|
Min. : 0.00
|
Min. : 0
|
Min. : 0.00
|
Min. : 0.00
|
Min. : 0.0
|
Min. : 32
|
Min. :-153.75
|
Min. :0.00
|
Min. :0.00
|
Min. :0.01
|
Min. : 0.94
|
Min. : 0.56
|
Min. : 0.40
|
Min. : 9
|
Min. :2.600e+01
|
Min. :2.000e+01
|
Min. :-2.300e+01
|
Min. : 0.03
|
Min. : 0.00
|
Min. : 0.00
|
Min. : 0.00
|
Min. :2.700e+01
|
Min. :1.800e+02
|
Min. :1.620e+02
|
Min. :-6.000e+00
|
Min. :0.000e+00
|
Min. :8.800e+01
|
Min. : 0.00
|
Min. : 0.0
|
Min. :0.000e+00
|
Min. : 0.00
|
Min. : 0.00
|
Min. : 0
|
Min. : 2.54
|
Min. :-166.93
|
Min. :0.00
|
Min. :0.00
|
Min. :0.01
|
Min. : 0.93
|
Min. : 0.56
|
Min. : 0.40
|
Min. : 5.84
|
Min. : 2.76
|
Min. : 0.91
|
Min. : 3.76
|
Min. : 0.49
|
Min. : 3.98
|
Min. : 0.97
|
Min. : 2.62
|
Min. : 0.00
|
Min. : 1.59
|
Min. : 3.18
|
Min. : 0.05
|
Min. : 2.17
|
Min. : 0.76
|
Min. : 2.71
|
Min. : 2.17
|
Min. :0.67
|
Min. : 0.88
|
Min. : 4.53
|
Min. : 0.01
|
Min. : 3.21
|
Min. : 0.78
|
Min. : 0.95
|
Min. : 1.99
|
Min. : 0.53
|
Min. : 2.33
|
Min. : 0.46
|
Min. : 1.96
|
Min. : 0.00
|
Min. : 0.78
|
Min. : 0.98
|
Min. : 0.03
|
Min. : 0.77
|
Min. : 0.64
|
Min. : 1.68
|
Min. : 0.78
|
Min. :0.61
|
Min. : 0.40
|
Min. : 2.61
|
Min. : 0.01
|
DELFWT:585339
|
PHDELWT:585339
|
Mode:logical
|
Min. :0.2
|
Min. :1.9
|
Min. :1.4
|
Min. :1
|
Min. :0.4801
|
Min. :-1.942e-07
|
Min. :0.00125
|
Min. :0.0000016
|
Min. :-10.82110
|
Min. : 0.00718
|
Min. :2.8
|
Min. :4.05
|
Min. :0.5
|
|
|
Class :character
|
Class :character
|
Class :character
|
1rwt : 817
|
GOL : 40606
|
401 : 15550
|
B :131529
|
1st Qu.:0.50000
|
1st Qu.:0.0000
|
1st Qu.:0.249295
|
1st Qu.:0.00000
|
1st Qu.: 4.0
|
1st Qu.: 4.00
|
1st Qu.: 3.75
|
1st Qu.: 30.0
|
1st Qu.: 28.00
|
1st Qu.: 0.000
|
1st Qu.: 0.000
|
1st Qu.: 1.000
|
1st Qu.: 0.0000
|
1st Qu.: 4.00
|
1st Qu.: 30
|
1st Qu.: 0.000
|
1st Qu.: 0.000
|
1st Qu.: 1.000
|
1st Qu.: 0.000
|
Class :character
|
1st Qu.: 0.0000
|
1st Qu.: 2.00
|
1st Qu.: 0.00000
|
1st Qu.: 0.0000
|
1st Qu.: 0.000
|
1st Qu.: 0.0000
|
1st Qu.: 0.0000
|
1st Qu.:1.333
|
1st Qu.: 0.0000
|
1st Qu.: 0.000
|
1st Qu.: 0.000
|
1st Qu.: 2.00
|
1st Qu.: 1.00
|
1st Qu.: 0.0000
|
1st Qu.: 0.000
|
1st Qu.: 0.0000
|
1st Qu.: 0.000
|
1st Qu.: 0.000
|
1st Qu.: 0.000
|
1st Qu.:0.0000000
|
1st Qu.: 0.000
|
1st Qu.: 0.00
|
1st Qu.: 1.000
|
1st Qu.: 0.0000
|
1st Qu.:0.02564
|
1st Qu.: 0.000
|
1st Qu.: 0.0000
|
1st Qu.:0.0000
|
1st Qu.: 2.000
|
1st Qu.: 1.00
|
1st Qu.: 0.000
|
1st Qu.:2.000
|
1st Qu.: 3.00
|
1st Qu.: 0.00
|
1st Qu.: 0.0000
|
1st Qu.: 0.000
|
1st Qu.: 2.000
|
1st Qu.: 209.95
|
1st Qu.: 3.4240
|
1st Qu.:0.0120943
|
1st Qu.:0.0683722
|
1st Qu.:0
|
1st Qu.: 0.56617
|
1st Qu.: 5.243
|
1st Qu.:0.120913
|
1st Qu.: 0.0000
|
1st Qu.: 0.000
|
1st Qu.: 0.00000
|
1st Qu.: 0.0000
|
1st Qu.: 0.00
|
1st Qu.: 0.000
|
1st Qu.: 5.0
|
1st Qu.: 5.0
|
1st Qu.: 6.76
|
1st Qu.: 3.407
|
1st Qu.:0.3637
|
1st Qu.:0.06447
|
1st Qu.: 0.5662
|
1st Qu.: 5.243
|
1st Qu.: 0.05617
|
1st Qu.: 1.000
|
1st Qu.:2.636e+04
|
1st Qu.:1.727e+08
|
1st Qu.:3.130e+11
|
1st Qu.:8.368e+08
|
1st Qu.: 0.2698
|
1st Qu.:0.02160
|
1st Qu.:0.000511
|
1st Qu.: 0.00061
|
1st Qu.:1.149e+06
|
1st Qu.:2.021e+11
|
1st Qu.:4.893e+11
|
1st Qu.:4.277e+08
|
1st Qu.:6.515e+07
|
1st Qu.:1.346e+10
|
1st Qu.: 0.0981
|
1st Qu.: 0.00193
|
1st Qu.: 0
|
1st Qu.: 0.00028
|
1st Qu.: 0.00004
|
1st Qu.: 0.01
|
1st Qu.: 845
|
1st Qu.: -0.77772
|
1st Qu.:0.087185
|
1st Qu.:0.216446
|
1st Qu.:0.37203
|
1st Qu.: 4.005
|
1st Qu.: 2.5797
|
1st Qu.: 1.9492
|
1st Qu.: 12674
|
1st Qu.:3.967e+07
|
1st Qu.:3.448e+10
|
1st Qu.: 1.568e+08
|
1st Qu.: 0.3822
|
1st Qu.: 0.04116
|
1st Qu.: 0.001244
|
1st Qu.: 0.002
|
1st Qu.:5.256e+05
|
1st Qu.:4.199e+10
|
1st Qu.:1.030e+11
|
1st Qu.: 8.363e+07
|
1st Qu.:1.798e+07
|
1st Qu.:2.962e+09
|
1st Qu.: 0.22
|
1st Qu.: 0.009
|
1st Qu.:0.000e+00
|
1st Qu.: 0.001
|
1st Qu.: 0.000
|
1st Qu.: 0
|
1st Qu.: 425.88
|
1st Qu.: -0.84098
|
1st Qu.:0.083809
|
1st Qu.:0.211683
|
1st Qu.:0.37111
|
1st Qu.: 3.742
|
1st Qu.: 2.4413
|
1st Qu.: 1.8649
|
1st Qu.: 15.435
|
1st Qu.: 14.203
|
1st Qu.: 6.4903
|
1st Qu.: 9.847
|
1st Qu.: 5.9165
|
1st Qu.: 14.52
|
1st Qu.: 11.916
|
1st Qu.: 11.226
|
1st Qu.: 5.44450
|
1st Qu.: 19.26
|
1st Qu.: 18.555
|
1st Qu.: 14.25
|
1st Qu.: 16.255
|
1st Qu.: 5.5867
|
1st Qu.: 11.329
|
1st Qu.: 19.038
|
1st Qu.:1.2639
|
1st Qu.: 15.87
|
1st Qu.: 13.111
|
1st Qu.: 8.12327
|
1st Qu.: 10.572
|
1st Qu.: 10.0832
|
1st Qu.: 5.9828
|
1st Qu.: 7.446
|
1st Qu.: 4.3896
|
1st Qu.: 9.895
|
1st Qu.: 7.5226
|
1st Qu.: 7.490
|
1st Qu.: 3.55351
|
1st Qu.: 14.1608
|
1st Qu.: 11.9833
|
1st Qu.: 10.9179
|
1st Qu.: 10.5972
|
1st Qu.: 5.1127
|
1st Qu.: 8.136
|
1st Qu.: 14.1157
|
1st Qu.:1.2520
|
1st Qu.: 12.4357
|
1st Qu.: 9.293
|
1st Qu.: 6.98238
|
1st Qu.: 3.0
|
1st Qu.: 3.0
|
1st Qu.: 4.176
|
1st Qu.: 2.278
|
1st Qu.:0.3979
|
1st Qu.:0.05228
|
1st Qu.: 0.5654
|
1st Qu.: 5.243
|
1st Qu.: 0.04405
|
1st Qu.: 1.000
|
1st Qu.:1.272e+04
|
1st Qu.:3.905e+07
|
1st Qu.:3.340e+10
|
1st Qu.:8.377e+07
|
1st Qu.: 0.259
|
1st Qu.: 0.020
|
1st Qu.:0.000
|
1st Qu.: 0.000
|
1st Qu.:4.038e+05
|
1st Qu.:2.451e+10
|
1st Qu.:5.759e+10
|
1st Qu.:4.117e+07
|
1st Qu.:4.750e+06
|
1st Qu.:2.276e+09
|
1st Qu.: 0.088
|
1st Qu.: 0.002
|
1st Qu.: 0
|
1st Qu.: 0.000
|
1st Qu.: 0.000
|
1st Qu.: 0.01
|
1st Qu.: 538
|
1st Qu.: -0.605
|
1st Qu.:0.080
|
1st Qu.:0.207
|
1st Qu.:0.382
|
1st Qu.: 3.372
|
1st Qu.: 2.192
|
1st Qu.: 1.680
|
1st Qu.: 6869
|
1st Qu.:1.122e+07
|
1st Qu.:5.088e+09
|
1st Qu.: 2.062e+07
|
1st Qu.: 0.353
|
1st Qu.: 0.036
|
1st Qu.: 0.001
|
1st Qu.: 0.001
|
1st Qu.:2.112e+05
|
1st Qu.:6.678e+09
|
1st Qu.:1.563e+10
|
1st Qu.: 1.036e+07
|
1st Qu.:1.766e+06
|
1st Qu.:6.473e+08
|
1st Qu.: 0.18
|
1st Qu.: 0.01
|
1st Qu.:0.000e+00
|
1st Qu.: 0.000
|
1st Qu.: 0.000
|
1st Qu.: 0
|
1st Qu.: 294.36
|
1st Qu.: -0.641
|
1st Qu.:0.078
|
1st Qu.:0.204
|
1st Qu.:0.383
|
1st Qu.: 3.176
|
1st Qu.: 2.100
|
1st Qu.: 1.625
|
1st Qu.: 11.966
|
1st Qu.: 11.333
|
1st Qu.: 6.614
|
1st Qu.: 8.451
|
1st Qu.: 4.621
|
1st Qu.: 10.660
|
1st Qu.: 8.959
|
1st Qu.: 8.534
|
1st Qu.: 4.158
|
1st Qu.: 14.970
|
1st Qu.: 13.815
|
1st Qu.: 10.799
|
1st Qu.: 11.976
|
1st Qu.: 5.426
|
1st Qu.: 8.152
|
1st Qu.: 14.08
|
1st Qu.:1.305
|
1st Qu.: 11.216
|
1st Qu.: 10.265
|
1st Qu.: 5.811
|
1st Qu.: 9.308
|
1st Qu.: 8.383
|
1st Qu.: 6.265
|
1st Qu.: 7.426
|
1st Qu.: 4.056
|
1st Qu.: 8.088
|
1st Qu.: 5.805
|
1st Qu.: 6.302
|
1st Qu.: 2.814
|
1st Qu.: 11.637
|
1st Qu.: 9.125
|
1st Qu.: 8.735
|
1st Qu.: 7.873
|
1st Qu.: 5.209
|
1st Qu.: 6.744
|
1st Qu.: 10.56
|
1st Qu.:1.293
|
1st Qu.: 8.976
|
1st Qu.: 8.412
|
1st Qu.: 4.625
|
1st Qu.: 2.0
|
1st Qu.: 2.0
|
1st Qu.: 2.344
|
1st Qu.: 1.365
|
1st Qu.:0.4099
|
1st Qu.:0.03952
|
1st Qu.: 0.5484
|
1st Qu.: 5.243
|
1st Qu.: 0.03205
|
1st Qu.: 1.0
|
1st Qu.:7.513e+03
|
1st Qu.:1.347e+07
|
1st Qu.:6.791e+09
|
1st Qu.: 1.375e+07
|
1st Qu.: 0.25
|
1st Qu.: 0.02
|
1st Qu.:0.00
|
1st Qu.: 0.00
|
1st Qu.:1.827e+05
|
1st Qu.:5.239e+09
|
1st Qu.:1.094e+10
|
1st Qu.: 6.426e+06
|
1st Qu.:5.776e+05
|
1st Qu.:5.934e+08
|
1st Qu.: 0.08
|
1st Qu.: 0.00
|
1st Qu.: 0
|
1st Qu.: 0.00
|
1st Qu.: 0.00
|
1st Qu.: 0.0
|
1st Qu.: 391
|
1st Qu.: -0.45
|
1st Qu.:0.08
|
1st Qu.:0.21
|
1st Qu.:0.39
|
1st Qu.: 2.93
|
1st Qu.: 1.96
|
1st Qu.: 1.52
|
1st Qu.: 4446
|
1st Qu.:4.621e+06
|
1st Qu.:1.340e+09
|
1st Qu.: 4.467e+06
|
1st Qu.: 0.32
|
1st Qu.: 0.03
|
1st Qu.: 0.00
|
1st Qu.: 0.00
|
1st Qu.:1.063e+05
|
1st Qu.:1.777e+09
|
1st Qu.:3.708e+09
|
1st Qu.: 2.136e+06
|
1st Qu.:2.945e+05
|
1st Qu.:2.073e+08
|
1st Qu.: 0.14
|
1st Qu.: 0.0
|
1st Qu.:0.000e+00
|
1st Qu.: 0.00
|
1st Qu.: 0.00
|
1st Qu.: 0
|
1st Qu.: 232.72
|
1st Qu.: -0.47
|
1st Qu.:0.07
|
1st Qu.:0.20
|
1st Qu.:0.40
|
1st Qu.: 2.79
|
1st Qu.: 1.89
|
1st Qu.: 1.48
|
1st Qu.: 10.89
|
1st Qu.: 9.66
|
1st Qu.: 6.78
|
1st Qu.: 8.42
|
1st Qu.: 4.42
|
1st Qu.: 8.99
|
1st Qu.: 7.18
|
1st Qu.: 7.08
|
1st Qu.: 3.40
|
1st Qu.: 12.44
|
1st Qu.: 10.99
|
1st Qu.: 8.77
|
1st Qu.: 9.43
|
1st Qu.: 5.52
|
1st Qu.: 7.36
|
1st Qu.: 11.21
|
1st Qu.:1.34
|
1st Qu.: 8.87
|
1st Qu.: 9.71
|
1st Qu.: 4.59
|
1st Qu.: 9.34
|
1st Qu.: 7.45
|
1st Qu.: 6.52
|
1st Qu.: 7.67
|
1st Qu.: 4.13
|
1st Qu.: 7.66
|
1st Qu.: 5.02
|
1st Qu.: 5.80
|
1st Qu.: 2.42
|
1st Qu.: 10.16
|
1st Qu.: 7.71
|
1st Qu.: 7.45
|
1st Qu.: 6.59
|
1st Qu.: 5.36
|
1st Qu.: 6.66
|
1st Qu.: 8.37
|
1st Qu.:1.33
|
1st Qu.: 6.76
|
1st Qu.: 8.57
|
1st Qu.: 3.28
|
NA
|
NA
|
NA’s:585339
|
1st Qu.:0.2
|
1st Qu.:1.9
|
1st Qu.:1.4
|
1st Qu.:1
|
1st Qu.:1.8000
|
1st Qu.:-4.620e-11
|
1st Qu.:0.09028
|
1st Qu.:0.0081507
|
1st Qu.: -0.84898
|
1st Qu.: 1.14347
|
1st Qu.:2.8
|
1st Qu.:4.05
|
1st Qu.:0.5
|
|
|
Mode :character
|
Mode :character
|
Mode :character
|
4il6 : 464
|
EDO : 30825
|
201 : 14771
|
C : 50058
|
Median :0.72662
|
Median :0.0000
|
Median :0.463163
|
Median :0.00000
|
Median : 6.0
|
Median : 6.00
|
Median : 6.00
|
Median : 48.0
|
Median : 48.00
|
Median : 3.000
|
Median : 0.000
|
Median : 3.000
|
Median : 0.0000
|
Median : 6.00
|
Median : 48
|
Median : 3.000
|
Median : 0.000
|
Median : 3.000
|
Median : 0.000
|
Mode :character
|
Median : 0.0000
|
Median : 13.00
|
Median : 0.00000
|
Median : 0.0000
|
Median : 0.000
|
Median : 0.0000
|
Median : 0.0000
|
Median :1.867
|
Median : 0.0000
|
Median : 0.000
|
Median : 0.000
|
Median : 14.00
|
Median : 7.00
|
Median : 0.0000
|
Median : 0.000
|
Median : 0.0000
|
Median : 0.000
|
Median : 0.000
|
Median : 0.000
|
Median :0.0000000
|
Median : 0.000
|
Median : 0.00
|
Median : 1.000
|
Median : 0.0000
|
Median :0.09091
|
Median : 0.000
|
Median : 0.0000
|
Median :0.0000
|
Median : 2.000
|
Median : 12.00
|
Median : 0.000
|
Median :2.000
|
Median : 15.00
|
Median : 0.00
|
Median : 0.0000
|
Median : 3.000
|
Median : 2.000
|
Median : 342.72
|
Median : 7.7073
|
Median :0.0186029
|
Median :0.0985439
|
Median :0
|
Median : 0.89061
|
Median : 7.251
|
Median :0.174443
|
Median : 0.0000
|
Median : 2.000
|
Median : 0.00000
|
Median : 0.0000
|
Median : 1.00
|
Median : 1.000
|
Median : 25.0
|
Median : 25.0
|
Median : 14.09
|
Median : 7.642
|
Median :0.5154
|
Median :0.12151
|
Median : 0.8906
|
Median : 7.251
|
Median : 0.11127
|
Median : 1.000
|
Median :9.455e+04
|
Median :2.210e+09
|
Median :1.421e+13
|
Median :3.627e+10
|
Median : 0.3815
|
Median :0.03363
|
Median :0.000765
|
Median : 0.00552
|
Median :7.535e+06
|
Median :8.364e+12
|
Median :2.320e+13
|
Median :1.985e+10
|
Median :5.985e+09
|
Median :3.235e+11
|
Median : 0.2298
|
Median : 0.00661
|
Median : 0
|
Median : 0.00311
|
Median : 0.00106
|
Median : 0.04
|
Median : 1761
|
Median : 0.00028
|
Median :0.171481
|
Median :0.384132
|
Median :0.57391
|
Median : 5.856
|
Median : 3.4955
|
Median : 2.5748
|
Median : 46240
|
Median :5.300e+08
|
Median :1.649e+12
|
Median : 8.013e+09
|
Median : 0.6047
|
Median : 0.08751
|
Median : 0.003187
|
Median : 0.018
|
Median :3.349e+06
|
Median :1.660e+12
|
Median :4.597e+12
|
Median : 4.842e+09
|
Median :1.903e+09
|
Median :6.864e+10
|
Median : 0.57
|
Median : 0.045
|
Median :0.000e+00
|
Median : 0.011
|
Median : 0.005
|
Median : 0
|
Median : 955.32
|
Median : 0.00018
|
Median :0.170283
|
Median :0.384308
|
Median :0.57816
|
Median : 5.536
|
Median : 3.2623
|
Median : 2.4204
|
Median : 26.425
|
Median : 20.504
|
Median : 10.0970
|
Median : 17.444
|
Median : 10.6313
|
Median : 24.59
|
Median : 20.707
|
Median : 18.005
|
Median : 9.82056
|
Median : 28.55
|
Median : 30.925
|
Median : 21.67
|
Median : 27.711
|
Median : 12.1386
|
Median : 20.353
|
Median : 31.148
|
Median :1.4068
|
Median : 26.81
|
Median : 23.158
|
Median : 14.75833
|
Median : 19.361
|
Median : 15.1019
|
Median : 8.4890
|
Median : 13.866
|
Median : 7.7504
|
Median : 17.683
|
Median : 17.0585
|
Median : 12.422
|
Median : 8.00466
|
Median : 21.3894
|
Median : 23.4385
|
Median : 16.8822
|
Median : 21.5871
|
Median : 9.7155
|
Median : 15.153
|
Median : 23.4279
|
Median :1.3955
|
Median : 20.8744
|
Median : 17.463
|
Median : 12.69851
|
Median : 15.0
|
Median : 15.0
|
Median : 10.208
|
Median : 6.073
|
Median :0.5639
|
Median :0.10857
|
Median : 0.8904
|
Median : 7.251
|
Median : 0.09744
|
Median : 1.000
|
Median :5.684e+04
|
Median :7.978e+08
|
Median :3.085e+12
|
Median :9.336e+09
|
Median : 0.364
|
Median : 0.031
|
Median :0.001
|
Median : 0.004
|
Median :3.674e+06
|
Median :1.982e+12
|
Median :5.369e+12
|
Median :5.202e+09
|
Median :1.418e+09
|
Median :9.422e+10
|
Median : 0.211
|
Median : 0.005
|
Median : 0
|
Median : 0.003
|
Median : 0.001
|
Median : 0.04
|
Median : 1295
|
Median : 0.000
|
Median :0.180
|
Median :0.394
|
Median :0.595
|
Median : 5.202
|
Median : 3.132
|
Median : 2.331
|
Median : 31721
|
Median :2.424e+08
|
Median :5.054e+11
|
Median : 2.571e+09
|
Median : 0.563
|
Median : 0.077
|
Median : 0.003
|
Median : 0.012
|
Median :1.907e+06
|
Median :5.372e+11
|
Median :1.421e+12
|
Median : 1.533e+09
|
Median :5.403e+08
|
Median :2.723e+10
|
Median : 0.49
|
Median : 0.03
|
Median :0.000e+00
|
Median : 0.007
|
Median : 0.003
|
Median : 0
|
Median : 772.52
|
Median : 0.000
|
Median :0.181
|
Median :0.395
|
Median :0.599
|
Median : 4.895
|
Median : 2.939
|
Median : 2.211
|
Median : 21.563
|
Median : 17.583
|
Median : 8.580
|
Median : 14.111
|
Median : 8.877
|
Median : 20.190
|
Median : 16.674
|
Median : 15.254
|
Median : 8.060
|
Median : 23.921
|
Median : 25.292
|
Median : 17.912
|
Median : 22.317
|
Median : 9.582
|
Median : 16.421
|
Median : 25.32
|
Median :1.499
|
Median : 21.433
|
Median : 18.554
|
Median : 11.497
|
Median : 16.289
|
Median : 13.580
|
Median : 7.720
|
Median : 11.408
|
Median : 6.806
|
Median : 15.153
|
Median : 13.700
|
Median : 11.053
|
Median : 6.125
|
Median : 18.961
|
Median : 19.529
|
Median : 14.734
|
Median : 17.718
|
Median : 8.135
|
Median : 12.738
|
Median : 20.13
|
Median :1.490
|
Median : 17.699
|
Median : 14.446
|
Median : 10.408
|
Median : 8.0
|
Median : 8.0
|
Median : 7.272
|
Median : 4.713
|
Median :0.6035
|
Median :0.09547
|
Median : 0.8870
|
Median : 7.251
|
Median : 0.08381
|
Median : 1.0
|
Median :3.960e+04
|
Median :3.851e+08
|
Median :1.028e+12
|
Median : 3.176e+09
|
Median : 0.34
|
Median : 0.03
|
Median :0.00
|
Median : 0.00
|
Median :2.179e+06
|
Median :6.930e+11
|
Median :1.816e+12
|
Median : 1.746e+09
|
Median :3.745e+08
|
Median :3.841e+10
|
Median : 0.18
|
Median : 0.00
|
Median : 0
|
Median : 0.00
|
Median : 0.00
|
Median : 0.0
|
Median : 1033
|
Median : 0.00
|
Median :0.20
|
Median :0.42
|
Median :0.62
|
Median : 4.66
|
Median : 2.89
|
Median : 2.18
|
Median : 24555
|
Median :1.431e+08
|
Median :2.283e+11
|
Median : 1.065e+09
|
Median : 0.51
|
Median : 0.06
|
Median : 0.00
|
Median : 0.01
|
Median :1.294e+06
|
Median :2.408e+11
|
Median :6.220e+11
|
Median : 6.115e+08
|
Median :1.726e+08
|
Median :1.439e+10
|
Median : 0.39
|
Median : 0.0
|
Median :0.000e+00
|
Median : 0.00
|
Median : 0.00
|
Median : 0
|
Median : 677.95
|
Median : 0.00
|
Median :0.20
|
Median :0.42
|
Median :0.62
|
Median : 4.37
|
Median : 2.73
|
Median : 2.08
|
Median : 18.35
|
Median : 15.70
|
Median : 8.43
|
Median : 12.08
|
Median : 7.54
|
Median : 17.23
|
Median : 14.13
|
Median : 13.40
|
Median : 6.91
|
Median : 20.87
|
Median : 21.59
|
Median : 15.42
|
Median : 18.87
|
Median : 7.85
|
Median : 13.77
|
Median : 21.41
|
Median :1.59
|
Median : 17.81
|
Median : 15.66
|
Median : 9.41
|
Median : 14.36
|
Median : 12.72
|
Median : 8.03
|
Median : 10.25
|
Median : 6.16
|
Median : 13.51
|
Median : 11.23
|
Median : 10.21
|
Median : 5.13
|
Median : 17.47
|
Median : 16.83
|
Median : 13.41
|
Median : 15.03
|
Median : 7.39
|
Median : 11.17
|
Median : 17.88
|
Median :1.58
|
Median : 15.49
|
Median : 12.60
|
Median : 8.80
|
NA
|
NA
|
NA
|
Median :0.2
|
Median :1.9
|
Median :1.4
|
Median :1
|
Median :2.0700
|
Median : 8.000e-13
|
Median :0.12257
|
Median :0.0150225
|
Median : -0.66494
|
Median : 1.84918
|
Median :2.8
|
Median :4.05
|
Median :0.5
|
|
|
NA
|
NA
|
NA
|
3wu2 : 431
|
NAG : 26360
|
501 : 13832
|
D : 38783
|
Mean :0.66826
|
Mean :0.0199
|
Mean :0.500310
|
Mean :0.06738
|
Mean : 13.9
|
Mean : 13.56
|
Mean : 13.12
|
Mean : 100.5
|
Mean : 96.51
|
Mean : 7.775
|
Mean : 1.195
|
Mean : 3.778
|
Mean : 0.2177
|
Mean : 13.89
|
Mean : 103
|
Mean : 7.918
|
Mean : 1.172
|
Mean : 3.983
|
Mean : 0.217
|
NA
|
Mean : 0.3675
|
Mean : 24.14
|
Mean : 0.01258
|
Mean : 0.0042
|
Mean : 2.571
|
Mean : 0.0839
|
Mean : 0.0553
|
Mean :1.567
|
Mean : 0.9303
|
Mean : 3.465
|
Mean : 3.225
|
Mean : 35.39
|
Mean : 12.37
|
Mean : 0.1878
|
Mean : 5.239
|
Mean : 0.1462
|
Mean : 2.216
|
Mean : 3.454
|
Mean : 1.727
|
Mean :0.0001221
|
Mean : 1.828
|
Mean : 2.81
|
Mean : 1.616
|
Mean : 0.1148
|
Mean :0.22008
|
Mean : 2.813
|
Mean : 0.1191
|
Mean :0.1135
|
Mean : 3.217
|
Mean : 30.13
|
Mean : 1.982
|
Mean :1.893
|
Mean : 35.71
|
Mean : 0.68
|
Mean : 0.0527
|
Mean : 5.229
|
Mean : 2.002
|
Mean : 851.43
|
Mean : 17.6184
|
Mean :0.0234970
|
Mean :0.1225801
|
Mean :0
|
Mean : 1.35247
|
Mean : 9.750
|
Mean :0.222814
|
Mean : 0.3616
|
Mean : 4.372
|
Mean : 0.01279
|
Mean : 0.1215
|
Mean : 2.12
|
Mean : 2.146
|
Mean : 335.6
|
Mean : 335.6
|
Mean : 32.74
|
Mean : 17.422
|
Mean :0.6023
|
Mean :0.20956
|
Mean : 1.3525
|
Mean : 9.750
|
Mean : 0.21552
|
Mean : 1.071
|
Mean :1.669e+06
|
Mean :1.062e+13
|
Mean :1.785e+20
|
Mean :6.130e+16
|
Mean : 0.4920
|
Mean :0.06141
|
Mean :0.001972
|
Mean : 0.05614
|
Mean :2.952e+09
|
Mean :2.915e+20
|
Mean :8.551e+22
|
Mean :3.451e+16
|
Mean :1.665e+16
|
Mean :1.653e+18
|
Mean : 0.5528
|
Mean : 0.08990
|
Mean : 31
|
Mean : 0.03829
|
Mean : 0.02640
|
Mean : 0.85
|
Mean : 4093
|
Mean : 0.04576
|
Mean :0.244458
|
Mean :0.425595
|
Mean :0.55501
|
Mean : 7.997
|
Mean : 4.4171
|
Mean : 2.9344
|
Mean : 788428
|
Mean :1.342e+12
|
Mean :1.718e+18
|
Mean : 3.673e+15
|
Mean : 0.7472
|
Mean : 0.14645
|
Mean : 0.007762
|
Mean : 0.344
|
Mean :1.034e+09
|
Mean :1.362e+19
|
Mean :9.844e+20
|
Mean : 2.212e+15
|
Mean :1.237e+15
|
Mean :4.027e+16
|
Mean : 2.41
|
Mean : 0.739
|
Mean :1.761e+05
|
Mean : 0.276
|
Mean : 0.231
|
Mean : 292
|
Mean : 2177.81
|
Mean : 0.04725
|
Mean :0.247223
|
Mean :0.425723
|
Mean :0.55705
|
Mean : 7.680
|
Mean : 4.1928
|
Mean : 2.7711
|
Mean : 40.760
|
Mean : 26.090
|
Mean : 17.2744
|
Mean : 28.005
|
Mean : 14.9928
|
Mean : 34.79
|
Mean : 31.526
|
Mean : 24.292
|
Mean : 14.72483
|
Mean : 38.12
|
Mean : 46.273
|
Mean : 28.00
|
Mean : 41.788
|
Mean : 18.1950
|
Mean : 28.763
|
Mean : 43.564
|
Mean :1.4283
|
Mean : 37.48
|
Mean : 36.227
|
Mean : 20.37217
|
Mean : 30.192
|
Mean : 19.0086
|
Mean : 14.8229
|
Mean : 22.230
|
Mean :11.2677
|
Mean : 25.438
|
Mean : 24.6044
|
Mean : 17.242
|
Mean : 12.48289
|
Mean : 27.9393
|
Mean : 34.0886
|
Mean : 21.5159
|
Mean : 31.3630
|
Mean : 14.8567
|
Mean : 21.782
|
Mean : 32.1327
|
Mean :1.4189
|
Mean : 28.4110
|
Mean : 27.503
|
Mean : 16.94146
|
Mean : 280.4
|
Mean : 280.4
|
Mean : 25.174
|
Mean : 14.988
|
Mean :0.6539
|
Mean :0.19896
|
Mean : 1.3496
|
Mean : 9.719
|
Mean : 0.20253
|
Mean : 1.269
|
Mean :1.261e+06
|
Mean :5.858e+12
|
Mean :6.229e+19
|
Mean :3.420e+16
|
Mean : 0.531
|
Mean : 0.072
|
Mean :0.003
|
Mean : 0.136
|
Mean :2.210e+09
|
Mean :1.544e+20
|
Mean :5.458e+22
|
Mean :1.957e+16
|
Mean :9.808e+15
|
Mean :1.031e+18
|
Mean : 0.746
|
Mean : 0.229
|
Mean : 53
|
Mean : 0.110
|
Mean : 0.093
|
Mean : 1.41
|
Mean : 3176
|
Mean : 0.037
|
Mean :0.255
|
Mean :0.429
|
Mean :0.566
|
Mean : 7.420
|
Mean : 4.020
|
Mean : 2.658
|
Mean : 666175
|
Mean :9.895e+11
|
Mean :1.040e+18
|
Mean : 2.705e+15
|
Mean : 0.765
|
Mean : 0.154
|
Mean : 0.008
|
Mean : 0.743
|
Mean :8.687e+08
|
Mean :9.588e+18
|
Mean :7.258e+20
|
Mean : 1.676e+15
|
Mean :9.896e+14
|
Mean :3.008e+16
|
Mean : 2.62
|
Mean : 2.10
|
Mean :9.711e+04
|
Mean : 0.674
|
Mean : 0.628
|
Mean : 214
|
Mean : 1891.24
|
Mean : 0.037
|
Mean :0.257
|
Mean :0.430
|
Mean :0.568
|
Mean : 7.155
|
Mean : 3.838
|
Mean : 2.528
|
Mean : 35.639
|
Mean : 22.384
|
Mean : 15.906
|
Mean : 24.837
|
Mean : 13.361
|
Mean : 30.123
|
Mean : 27.194
|
Mean : 21.203
|
Mean : 13.007
|
Mean : 32.544
|
Mean : 39.928
|
Mean : 23.715
|
Mean : 35.874
|
Mean : 16.320
|
Mean : 24.835
|
Mean : 37.16
|
Mean :1.544
|
Mean : 31.719
|
Mean : 31.587
|
Mean : 17.298
|
Mean : 27.910
|
Mean : 17.248
|
Mean : 14.211
|
Mean : 20.708
|
Mean :10.646
|
Mean : 23.359
|
Mean : 22.003
|
Mean : 16.020
|
Mean : 11.288
|
Mean : 25.210
|
Mean : 30.780
|
Mean : 19.247
|
Mean : 28.110
|
Mean : 13.967
|
Mean : 19.925
|
Mean : 28.77
|
Mean :1.536
|
Mean : 25.184
|
Mean : 25.321
|
Mean : 14.897
|
Mean : 234.6
|
Mean : 234.6
|
Mean : 19.420
|
Mean : 12.800
|
Mean :0.6803
|
Mean :0.18783
|
Mean : 1.3233
|
Mean : 9.494
|
Mean : 0.18954
|
Mean : 1.3
|
Mean :1.005e+06
|
Mean :3.559e+12
|
Mean :2.610e+19
|
Mean : 2.044e+16
|
Mean : 0.57
|
Mean : 0.09
|
Mean :0.00
|
Mean : 0.35
|
Mean :1.746e+09
|
Mean :9.054e+19
|
Mean :3.549e+22
|
Mean : 1.201e+16
|
Mean :6.394e+15
|
Mean :6.688e+17
|
Mean : 1.03
|
Mean : 0.81
|
Mean : 177
|
Mean : 0.32
|
Mean : 0.29
|
Mean : 3.0
|
Mean : 2606
|
Mean : 0.03
|
Mean :0.27
|
Mean :0.44
|
Mean :0.58
|
Mean : 7.07
|
Mean : 3.78
|
Mean : 2.49
|
Mean : 585064
|
Mean :7.552e+11
|
Mean :6.637e+17
|
Mean : 2.064e+15
|
Mean : 0.77
|
Mean : 0.16
|
Mean : 0.01
|
Mean : 2.29
|
Mean :7.577e+08
|
Mean :7.098e+18
|
Mean :5.435e+20
|
Mean : 1.320e+15
|
Mean :8.244e+14
|
Mean :2.312e+16
|
Mean : 2.97
|
Mean : 11.3
|
Mean :1.465e+05
|
Mean : 2.35
|
Mean : 2.39
|
Mean : 250
|
Mean : 1717.53
|
Mean : 0.03
|
Mean :0.27
|
Mean :0.44
|
Mean :0.58
|
Mean : 6.83
|
Mean : 3.62
|
Mean : 2.38
|
Mean : 32.47
|
Mean : 20.00
|
Mean : 15.15
|
Mean : 22.96
|
Mean : 12.35
|
Mean : 27.20
|
Mean : 24.29
|
Mean : 19.24
|
Mean : 11.81
|
Mean : 28.87
|
Mean : 35.71
|
Mean : 20.90
|
Mean : 31.94
|
Mean : 15.19
|
Mean : 22.42
|
Mean : 32.92
|
Mean :1.66
|
Mean : 27.90
|
Mean : 28.75
|
Mean : 15.29
|
Mean : 26.74
|
Mean : 16.25
|
Mean : 14.06
|
Mean : 20.01
|
Mean :10.36
|
Mean : 22.23
|
Mean : 20.44
|
Mean : 15.37
|
Mean : 10.61
|
Mean : 23.57
|
Mean : 28.80
|
Mean : 17.89
|
Mean : 26.14
|
Mean : 13.57
|
Mean : 18.93
|
Mean : 26.69
|
Mean :1.65
|
Mean : 23.15
|
Mean : 24.20
|
Mean : 13.65
|
NA
|
NA
|
NA
|
Mean :0.2
|
Mean :1.9
|
Mean :1.4
|
Mean :1
|
Mean :2.1485
|
Mean : 4.470e-11
|
Mean :0.12905
|
Mean :0.0195631
|
Mean : -0.70075
|
Mean : 2.60360
|
Mean :2.8
|
Mean :4.05
|
Mean :0.5
|
|
|
NA
|
NA
|
NA
|
4pj0 : 353
|
CL : 23223
|
302 : 11002
|
E : 16206
|
3rd Qu.:0.86957
|
3rd Qu.:0.0000
|
3rd Qu.:0.713459
|
3rd Qu.:0.00000
|
3rd Qu.: 20.0
|
3rd Qu.: 19.00
|
3rd Qu.: 18.00
|
3rd Qu.: 133.0
|
3rd Qu.:126.00
|
3rd Qu.:10.000
|
3rd Qu.: 1.000
|
3rd Qu.: 5.000
|
3rd Qu.: 0.0000
|
3rd Qu.: 20.00
|
3rd Qu.: 136
|
3rd Qu.:10.000
|
3rd Qu.: 1.000
|
3rd Qu.: 6.000
|
3rd Qu.: 0.000
|
NA
|
3rd Qu.: 0.0000
|
3rd Qu.: 32.00
|
3rd Qu.: 0.00000
|
3rd Qu.: 0.0000
|
3rd Qu.: 0.000
|
3rd Qu.: 0.0000
|
3rd Qu.: 0.0000
|
3rd Qu.:1.962
|
3rd Qu.: 0.0000
|
3rd Qu.: 0.000
|
3rd Qu.: 0.000
|
3rd Qu.: 40.00
|
3rd Qu.: 17.00
|
3rd Qu.: 0.0000
|
3rd Qu.: 1.000
|
3rd Qu.: 0.0000
|
3rd Qu.: 0.000
|
3rd Qu.: 0.000
|
3rd Qu.: 0.000
|
3rd Qu.:0.0000000
|
3rd Qu.: 1.000
|
3rd Qu.: 0.00
|
3rd Qu.: 2.000
|
3rd Qu.: 0.0000
|
3rd Qu.:0.25000
|
3rd Qu.: 1.000
|
3rd Qu.: 0.0000
|
3rd Qu.:0.0000
|
3rd Qu.: 3.000
|
3rd Qu.: 35.00
|
3rd Qu.: 2.000
|
3rd Qu.:2.000
|
3rd Qu.: 40.00
|
3rd Qu.: 0.00
|
3rd Qu.: 0.0000
|
3rd Qu.: 11.000
|
3rd Qu.: 2.000
|
3rd Qu.: 782.08
|
3rd Qu.: 19.5602
|
3rd Qu.:0.0285268
|
3rd Qu.:0.1433580
|
3rd Qu.:0
|
3rd Qu.: 1.50809
|
3rd Qu.: 11.274
|
3rd Qu.:0.256536
|
3rd Qu.: 0.0240
|
3rd Qu.: 6.000
|
3rd Qu.: 0.00000
|
3rd Qu.: 0.0000
|
3rd Qu.: 3.00
|
3rd Qu.: 3.000
|
3rd Qu.: 139.0
|
3rd Qu.: 139.0
|
3rd Qu.: 34.18
|
3rd Qu.: 18.981
|
3rd Qu.:0.7187
|
3rd Qu.:0.23400
|
3rd Qu.: 1.5081
|
3rd Qu.: 11.274
|
3rd Qu.: 0.23399
|
3rd Qu.: 1.000
|
3rd Qu.:5.838e+05
|
3rd Qu.:7.047e+10
|
3rd Qu.:2.030e+15
|
3rd Qu.:5.090e+12
|
3rd Qu.: 0.6068
|
3rd Qu.:0.06885
|
3rd Qu.:0.001749
|
3rd Qu.: 0.03164
|
3rd Qu.:1.272e+08
|
3rd Qu.:1.848e+15
|
3rd Qu.:8.092e+15
|
3rd Qu.:3.046e+12
|
3rd Qu.:1.290e+12
|
3rd Qu.:3.792e+13
|
3rd Qu.: 0.5898
|
3rd Qu.: 0.03465
|
3rd Qu.: 0
|
3rd Qu.: 0.01957
|
3rd Qu.: 0.00950
|
3rd Qu.: 0.19
|
3rd Qu.: 4273
|
3rd Qu.: 0.82325
|
3rd Qu.:0.360112
|
3rd Qu.:0.618134
|
3rd Qu.:0.74494
|
3rd Qu.: 9.976
|
3rd Qu.: 5.2246
|
3rd Qu.: 3.4617
|
3rd Qu.: 257105
|
3rd Qu.:1.350e+10
|
3rd Qu.:1.686e+14
|
3rd Qu.: 1.066e+12
|
3rd Qu.: 0.9580
|
3rd Qu.: 0.17921
|
3rd Qu.: 0.007750
|
3rd Qu.: 0.127
|
3rd Qu.:5.470e+07
|
3rd Qu.:3.311e+14
|
3rd Qu.:1.542e+15
|
3rd Qu.: 7.112e+11
|
3rd Qu.:3.701e+11
|
3rd Qu.:7.337e+12
|
3rd Qu.: 1.52
|
3rd Qu.: 0.237
|
3rd Qu.:1.000e+00
|
3rd Qu.: 0.085
|
3rd Qu.: 0.049
|
3rd Qu.: 1
|
3rd Qu.: 2372.66
|
3rd Qu.: 0.88385
|
3rd Qu.:0.369874
|
3rd Qu.:0.622709
|
3rd Qu.:0.75041
|
3rd Qu.: 9.651
|
3rd Qu.: 4.9417
|
3rd Qu.: 3.2234
|
3rd Qu.: 52.866
|
3rd Qu.: 31.939
|
3rd Qu.: 21.8753
|
3rd Qu.: 36.564
|
3rd Qu.: 19.4280
|
3rd Qu.: 44.81
|
3rd Qu.: 41.863
|
3rd Qu.: 30.606
|
3rd Qu.: 19.06451
|
3rd Qu.: 47.73
|
3rd Qu.: 60.562
|
3rd Qu.: 35.18
|
3rd Qu.: 55.044
|
3rd Qu.: 24.1803
|
3rd Qu.: 37.437
|
3rd Qu.: 56.348
|
3rd Qu.:1.5706
|
3rd Qu.: 49.14
|
3rd Qu.: 47.297
|
3rd Qu.: 27.11986
|
3rd Qu.: 38.983
|
3rd Qu.: 23.7998
|
3rd Qu.: 18.7919
|
3rd Qu.: 28.952
|
3rd Qu.:14.4114
|
3rd Qu.: 32.914
|
3rd Qu.: 33.3966
|
3rd Qu.: 21.831
|
3rd Qu.: 17.08645
|
3rd Qu.: 34.6093
|
3rd Qu.: 44.8828
|
3rd Qu.: 27.5450
|
3rd Qu.: 41.6574
|
3rd Qu.: 19.3818
|
3rd Qu.: 28.500
|
3rd Qu.: 41.2626
|
3rd Qu.:1.5651
|
3rd Qu.: 36.8588
|
3rd Qu.: 35.746
|
3rd Qu.: 22.85958
|
3rd Qu.: 93.0
|
3rd Qu.: 93.0
|
3rd Qu.: 25.520
|
3rd Qu.: 16.495
|
3rd Qu.:0.7851
|
3rd Qu.:0.22254
|
3rd Qu.: 1.5081
|
3rd Qu.: 11.274
|
3rd Qu.: 0.21924
|
3rd Qu.: 1.000
|
3rd Qu.:3.756e+05
|
3rd Qu.:2.868e+10
|
3rd Qu.:5.208e+14
|
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(Other):386715
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(Other):501104
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(Other): 59264
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NA
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NA’s :11007
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
|
NA
|
NA
|
NA
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NA
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NA
|
NA
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NA
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NA
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NA
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NA
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NA’s :2
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NA
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NA’s :7
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NA’s :7
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NA’s :7
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NA’s :7
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NA’s :7
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NA’s :7
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NA’s :7
|
NA’s :7
|
NA’s :7
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NA’s :7
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NA’s :7
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NA’s :7
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NA’s :7
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NA’s :7
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA’s :13
|
NA
|
NA’s :5482
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NA’s :5482
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NA’s :5482
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NA’s :5482
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NA’s :5482
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA
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NA’s :27
|
NA
|
NA’s :40036
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NA’s :40036
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NA’s :40036
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NA’s :40036
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NA
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Dalsze przetwarzanie i analiza danych
Ograniczenie liczby klas (res_name) do 50 najpopularniejszych wartości
top50 <- All_Data %>% group_by(res_name) %>% summarise(n = n()) %>% arrange(desc(n)) %>% head(50)
top50 <- as.array(top50$res_name)
kable(top50)%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
|
x
|
|
SO4
|
|
GOL
|
|
EDO
|
|
NAG
|
|
CL
|
|
CA
|
|
ZN
|
|
MG
|
|
HEM
|
|
PO4
|
|
NA
|
|
ACT
|
|
DMS
|
|
IOD
|
|
PEG
|
|
CLA
|
|
K
|
|
FAD
|
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NAD
|
|
MN
|
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ADP
|
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MLY
|
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NAP
|
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CD
|
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MPD
|
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FMT
|
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MAN
|
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PG4
|
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MES
|
|
CU
|
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ATP
|
|
COA
|
|
1PE
|
|
BR
|
|
NDP
|
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FMN
|
|
EPE
|
|
HEC
|
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PGE
|
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TRS
|
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SF4
|
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NI
|
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ACY
|
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FE
|
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NO3
|
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PLP
|
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GDP
|
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SAH
|
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FE2
|
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SEP
|
All_Data <- All_Data %>% filter(res_name %in% top50)
Liczba przykładów
examples <- All_Data %>% group_by(res_name) %>% summarise(n = n()) %>% arrange(desc(n))
kable(examples)%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
|
res_name
|
n
|
|
SO4
|
56572
|
|
GOL
|
40606
|
|
EDO
|
30825
|
|
NAG
|
26360
|
|
CL
|
23223
|
|
CA
|
21038
|
|
ZN
|
19826
|
|
MG
|
14779
|
|
HEM
|
11192
|
|
PO4
|
11090
|
|
NA
|
9613
|
|
ACT
|
8096
|
|
DMS
|
6633
|
|
IOD
|
6317
|
|
PEG
|
4987
|
|
CLA
|
4784
|
|
K
|
4706
|
|
FAD
|
4555
|
|
NAD
|
4501
|
|
MN
|
4215
|
|
ADP
|
3819
|
|
MLY
|
3509
|
|
NAP
|
3505
|
|
CD
|
3242
|
|
MPD
|
3221
|
|
FMT
|
2918
|
|
MAN
|
2841
|
|
PG4
|
2768
|
|
MES
|
2697
|
|
CU
|
2353
|
|
ATP
|
2296
|
|
COA
|
2183
|
|
1PE
|
2136
|
|
BR
|
2127
|
|
NDP
|
2106
|
|
FMN
|
2084
|
|
EPE
|
1933
|
|
HEC
|
1917
|
|
PGE
|
1905
|
|
TRS
|
1656
|
|
SF4
|
1647
|
|
NI
|
1637
|
|
ACY
|
1609
|
|
FE
|
1602
|
|
NO3
|
1596
|
|
PLP
|
1594
|
|
GDP
|
1589
|
|
SAH
|
1587
|
|
FE2
|
1560
|
|
SEP
|
1491
|
Wykresy rozkładów liczby atomów (local_res_atom_non_h_count) i elektronów (local_res_atom_non_h_electron_sum)
ggplot(All_Data, aes(local_res_atom_non_h_count)) + geom_histogram(binwidth = 1)

ggplot(All_Data, aes(local_res_atom_non_h_electron_sum)) + geom_histogram(binwidth = 1)

10 klas z największą niezgodnością liczby atomów
atom_count_diff <- All_Data %>%
mutate(diff = abs(local_res_atom_non_h_count - dict_atom_non_h_count)) %>%
group_by(res_name) %>%
summarise(mean_diff = mean(diff), sd_diff = sd(diff), min_diff = min(diff), max_diff = max(diff), n=n(), n_diff = sum(diff>0)) %>%
mutate(percent_diff = n_diff/n * 100) %>%
select(res_name, percent_diff) %>%
arrange(desc(percent_diff)) %>%
head(10) %>%
transmute(res_name, percent_diff = round(percent_diff, 2))
kable(atom_count_diff)%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
|
res_name
|
percent_diff
|
|
MLY
|
99.29
|
|
NAG
|
97.65
|
|
SEP
|
97.05
|
|
MAN
|
89.19
|
|
PLP
|
85.45
|
|
1PE
|
45.88
|
|
CLA
|
34.01
|
|
PG4
|
23.19
|
|
COA
|
9.62
|
|
NAP
|
8.73
|
10 klas z największą niezgodnością liczby elektronów
electron_count_diff <- All_Data %>%
mutate(diff = abs(local_res_atom_non_h_electron_sum - dict_atom_non_h_electron_sum)) %>%
group_by(res_name) %>%
summarise(mean_diff = mean(diff), sd_diff = sd(diff), min_diff = min(diff), max_diff = max(diff), n=n(), n_diff = sum(diff>0)) %>%
mutate(percent_diff = n_diff/n * 100) %>%
select(res_name, percent_diff) %>%
arrange(desc(percent_diff)) %>%
head(10) %>%
transmute(res_name, percent_diff = round(percent_diff, 2))
kable(electron_count_diff)%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
|
res_name
|
percent_diff
|
|
MLY
|
99.29
|
|
NAG
|
97.65
|
|
SEP
|
97.05
|
|
MAN
|
89.19
|
|
PLP
|
85.45
|
|
1PE
|
45.88
|
|
CLA
|
34.01
|
|
PG4
|
23.19
|
|
COA
|
9.62
|
|
NAP
|
8.73
|
Prezentacja niezgodności liczby elektronów
plot_ly(All_Data, x = ~local_res_atom_non_h_electron_sum, y = ~dict_atom_non_h_electron_sum, type="scattergl", mode="markers")
Prezentacja niezgodności liczby atomów
plot_ly(All_Data, x = ~local_res_atom_non_h_count, y = ~dict_atom_non_h_count, type="scattergl", mode="markers")
Rozkład wartości wszystkich kolumn zaczynających się od part_01
columns_part01 <- colnames(All_Data)
columns_part01 <- columns_part01[startsWith(columns_part01, "part_01")]
columns_part01 <- head(columns_part01, 10)
columns_part01_all_data <- All_Data %>% select(columns_part01)
data_gathered <- gather(columns_part01_all_data)
data_gathered_means <- data_gathered %>% group_by(key) %>% summarise(mean.value = mean(value, na.rm = TRUE))
ggplot(data_gathered, aes(value)) +
geom_histogram(bins = 10) +
facet_wrap(~key, scales = 'free_x',ncol = 2) +
geom_vline(data = data_gathered_means,aes(xintercept = mean.value), color="red", linetype="dashed", size=1) +
geom_text(data = data_gathered_means, aes(label=round(mean.value,2) ,y=0,x=mean.value), vjust=-1,col='orange',size=5)
## Warning: Removed 7 rows containing non-finite values (stat_bin).

Przygotowanie danych do regresji i klasyfikacji
manipulation_data <- All_Data
columns_to_predict <- colnames(manipulation_data)
columns_to_predict <- columns_to_predict[startsWith(columns_to_predict, "part_")]
manipulation_data <- manipulation_data %>% select(columns_to_predict)
numeric_data <- sapply(manipulation_data, class)
numeric_data <- numeric_data == "numeric" | numeric_data == "integer"
numeric_data <- manipulation_data[, numeric_data]
label_attributes <- c("local_res_atom_non_h_count", "local_res_atom_non_h_electron_sum", "res_name")
numeric_data[label_attributes] <- All_Data[label_attributes]
numeric_data <- numeric_data[complete.cases(numeric_data), ]
label_store <- numeric_data %>% select(label_attributes)
numeric_data <- numeric_data %>% select(-label_attributes)
q <- sapply(numeric_data, quantile, c(.05, .95) )
numeric_data <- as.data.frame(sapply(numeric_data, squish, q))
numeric_data <- numeric_data[ ,sapply(numeric_data, function(x) sd(quantile(x,c(.25, .75))) ) >0.1]
tmp <- cor(numeric_data)
#show cors
tmp2 <- tmp
tmp2[upper.tri(tmp2)] <- NA
diag(tmp2) <- NA
best_cor <- as.data.frame(as.table(tmp2)) %>% filter(!is.na(Freq)) %>% arrange(desc(abs(Freq))) %>% head(3)
for(d in c(1,2,3)) {
data_x_column <- as.character( best_cor[d,"Var1"])
data_y_column <- as.character( best_cor[d,"Var2"])
#print(paste(data_x_column,data_y_column))
print(qplot(x=numeric_data[,data_x_column], y=numeric_data[,data_y_column]))
}



worst_cor <- as.data.frame(as.table(tmp2)) %>% filter(!is.na(Freq)) %>% arrange(desc(abs(Freq))) %>% tail(3)
for(d in c(1,2,3)) {
data_x_column <- as.character( worst_cor[d,"Var1"])
data_y_column <- as.character( worst_cor[d,"Var2"])
print(qplot(x=numeric_data[,data_x_column], y=numeric_data[,data_y_column]))
}



tmp[upper.tri(tmp)] <- 0
diag(tmp) <- 0
cols_to_drop <- apply(tmp,2,function(x) any(abs(x) > 0.85))
data.new <- numeric_data[,!cols_to_drop]
data.new[label_attributes] <- label_store[label_attributes]
kable(summary(data.new))%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
|
|
part_01_density_Z_1_0
|
part_02_density_segments_count
|
part_02_max_over_std
|
part_02_shape_M000
|
part_02_density_M000
|
part_02_density_Z_1_0
|
part_02_density_Z_4_0
|
local_res_atom_non_h_count
|
local_res_atom_non_h_electron_sum
|
res_name </th>
|
|
|
Min. :1.000
|
Min. : 1.0
|
Min. : 3.834
|
Min. : 32.0
|
Min. : 2.538
|
Min. :1.000
|
Min. : 1.000
|
Min. : 1.000
|
Min. : 6.00
|
SO4 : 55070
|
|
|
1st Qu.:1.352
|
1st Qu.: 2.0
|
1st Qu.: 5.621
|
1st Qu.: 372.0
|
1st Qu.: 222.974
|
1st Qu.:1.422
|
1st Qu.: 2.877
|
1st Qu.: 1.000
|
1st Qu.: 28.00
|
GOL : 37281
|
|
|
Median :1.536
|
Median : 6.0
|
Median : 7.598
|
Median : 874.0
|
Median : 576.064
|
Median :1.668
|
Median : 7.239
|
Median : 5.000
|
Median : 42.00
|
EDO : 27993
|
|
|
Mean :1.549
|
Mean : 116.6
|
Mean : 10.343
|
Mean : 927.2
|
Mean : 755.862
|
Mean :1.703
|
Mean : 11.789
|
Mean : 9.053
|
Mean : 71.56
|
CL : 22692
|
|
|
3rd Qu.:1.734
|
3rd Qu.: 27.0
|
3rd Qu.: 12.095
|
3rd Qu.:1641.0
|
3rd Qu.:1401.333
|
3rd Qu.:1.906
|
3rd Qu.: 15.113
|
3rd Qu.: 7.000
|
3rd Qu.: 53.00
|
NAG : 22383
|
|
|
Max. :3.423
|
Max. :1641.0
|
Max. :133.748
|
Max. :1641.0
|
Max. :1641.000
|
Max. :5.051
|
Max. :107.269
|
Max. :65.000
|
Max. :410.00
|
CA : 20664
|
|
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
NA
|
(Other):171859
|
Przewidywanie liczby atomów
columns_part_all_data_predict <- data.new %>% select(-c("local_res_atom_non_h_electron_sum", "res_name"))
inTraining <-
createDataPartition(
y = columns_part_all_data_predict$local_res_atom_non_h_count,
p = .75,
list = FALSE)
training <- columns_part_all_data_predict[ inTraining,]
testing <- columns_part_all_data_predict[-inTraining,]
ctrl <- trainControl(method = "none")
fit_atom <- train(local_res_atom_non_h_count ~ .,
data = training,
method = "lm",
trControl = ctrl)
rfClasses <- predict(fit_atom, newdata = testing)
kable(postResample(rfClasses,testing$local_res_atom_non_h_count ))%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
|
|
x
|
|
RMSE
|
9.845877
|
|
Rsquared
|
0.432954
|
|
MAE
|
5.518124
|
Przewidywanie liczby elektronów
columns_part_all_data_predict <- data.new %>% select(-c("local_res_atom_non_h_count", "res_name"))
inTraining <-
createDataPartition(
y = columns_part_all_data_predict$local_res_atom_non_h_electron_sum,
p = .75,
list = FALSE)
training <- columns_part_all_data_predict[ inTraining,]
testing <- columns_part_all_data_predict[-inTraining,]
ctrl <- trainControl(method = "none")
fit_electron <- train(local_res_atom_non_h_electron_sum ~ .,
data = training,
method = "lm",
trControl = ctrl)
rfClasses <- predict(fit_electron, newdata = testing)
kable(postResample(rfClasses,testing$local_res_atom_non_h_electron_sum ))%>%
kable_styling() %>%
scroll_box(width = "100%", height = "600px")
|
|
x
|
|
RMSE
|
67.0337991
|
|
Rsquared
|
0.4213179
|
|
MAE
|
37.7833663
|
Przewidywanie atrybutu res_name
columns_part_all_data_predict <- data.new %>% select(-c("local_res_atom_non_h_count", "local_res_atom_non_h_electron_sum"))
columns_part_all_data_predict$res_name <- droplevels(columns_part_all_data_predict$res_name)
inTraining <-
createDataPartition(
y = columns_part_all_data_predict$res_name,
p = .75,
list = FALSE)
training <- columns_part_all_data_predict[ inTraining,]
testing <- columns_part_all_data_predict[-inTraining,]
ctrl <- trainControl(method = "none")
fit <- train(res_name ~ .,
data = training,
method = "rf",
trControl = ctrl,
ntree = 4)
rfClasses <- predict(fit, newdata = testing)
print(confusionMatrix(rfClasses,testing$res_name )$overall['Accuracy'] )
## Accuracy
## 0.2736408
columns_part_all_data_predict <- data.new %>% select(-c("local_res_atom_non_h_count", "local_res_atom_non_h_electron_sum"))
columns_part_all_data_predict$res_name <- droplevels(columns_part_all_data_predict$res_name)
predicted_electrons <- predict(fit_electron, newdata = columns_part_all_data_predict %>% select(-c( "res_name")))
predicted_atoms <- predict(fit_atom, newdata = columns_part_all_data_predict %>% select(-c("res_name")))
columns_part_all_data_predict <- data.frame(res_name = columns_part_all_data_predict$res_name, electrons = predicted_electrons, atoms = predicted_atoms)
inTraining <-
createDataPartition(
y = columns_part_all_data_predict$res_name,
p = .75,
list = FALSE)
training <- columns_part_all_data_predict[ inTraining,]
testing <- columns_part_all_data_predict[-inTraining,]
ctrl <- trainControl(method = "none")
fit <- train(res_name ~ .,
data = training,
method = "rf",
trControl = ctrl,
ntree = 4)
rfClasses <- predict(fit, newdata = testing)
print(confusionMatrix(rfClasses,testing$res_name )$overall['Accuracy'])
## Accuracy
## 0.1631494